부산대학교 도서관

In this paper, we present the Selective Redirection Controller (SRC), which selects the optimal redirection controller based on the physical and virtual environment in Redirected Walking (RDW). The primary advantage of SRC over existing controllers is its dynamic switching among four different redirection controllers (S2C, TAPF, ARC, and SRL) based on the user's environment, as opposed to using a single fixed controller throughout the experience. By switching between redirection controllers based on the context around the user, SRC aims to optimize the advantages of each redirection strategy. The SRC model is trained using reinforcement learning to dynamically and instantaneously switch redirection controllers based on the user's environment. We evaluated the performance of SRC against traditional redirection controllers through simulations and user studies conducted in various physical and virtual environments. The findings indicate that SRC reduces the number of resets significantly compared to traditional redirection controllers. Heat map visualization was utilized during the development process to analyze which redirection controller SRC chooses based on the different environments around the user. SRC alternates between redirection techniques based on the user's environment, maximizing the advantages of each strategy for a superior RDW experience.